AI Bias Adds Complexity To AI Systems

One of the biggest issues with Artificial Intelligence and Data Science is the integrity of our data. Even if we did all the right things in our models, and our testing and data might conform to a technical standard of "cleanliness," there might still be biases as well as "common sense" issues" that may come up. With Big Data, it is difficult to get to a certain granularity of data validity without proper, real-world testing. By real-world testing, we mean that when data is being used to make decisions, as consumers, testers, programmers, and data scientists, we look at groups of scenarios to see if the decisions are made to conform to a standard of "common sense". This means when we discover the most important biases in our data.